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Optogenetics-enabled assessment of viral gene and cell therapy for restoration of cardiac excitability.

Ambrosi CM, Boyle PM, Chen K, Trayanova NA, Entcheva E - Sci Rep (2015)

Bottom Line: Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias).Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation.More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY.

ABSTRACT
Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias). In addition to electronic device therapy (i.e. implantable pacemakers and cardioverter/defibrillators), biological approaches have recently been explored to restore pacemaking ability and to correct conduction slowing in the heart by delivering excitatory ion channels or ion channel agonists. Using optogenetics as a tool to selectively interrogate only cells transduced to produce an exogenous excitatory ion current, we experimentally and computationally quantify the efficiency of such biological approaches in rescuing cardiac excitability as a function of the mode of application (viral gene delivery or cell delivery) and the geometry of the transduced region (focal or spatially-distributed). We demonstrate that for each configuration (delivery mode and spatial pattern), the optical energy needed to excite can be used to predict therapeutic efficiency of excitability restoration. Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation. More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

No MeSH data available.


Related in: MedlinePlus

Patterned light-sensitive cardiac syncytia in silico.(a–f) Computational models of light-sensitive cell monolayers with light sensitivity inscribed by gene delivery (GD; a–c) or cell delivery (CD; d–f). Heterogeneous spatial distributions of ChR2-expressing cells (transduced myocytes in (a–c), donor cells in (d–f)) were generated using a previously-described stochastic algorithm25; for each distribution type (I, UL, UH) and delivery mode (CD or GD), parameter values for density and clustering (D and C; shown in black boxes) were chosen to ensure a good match between in silico and in vitro monolayers (compare (a–f) to Fig. 2a–f) For GD-I (a), the algorithm was invoked with distinct parameter combinations in 3 distinct regions: the central target (blue shaded; 1.6% of total monolayer area), an outer ring (red shaded; 1.3% of monolayer area), and the surrounding area. For CD-I (d), model generation involved two invocations of the distribution algorithm: first, to produce a large central cluster (blue; 4.5% of monolayer are); second, to deliver light-sensitive cell clusters to the peripheral region. (g) Normalized histogram showing occurrence rates of light-sensitive cell cluster sizes for in vitro monolayers of the UL spatial distribution; histograms for GD (Fig. 2b) and CD (Fig. 2e) modes are compared. (h) Same as (g), but for in silico cell monolayer models. Main panel scale bars in (a–f) are 2 mm; inset panel scale bars are 1 mm.
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f3: Patterned light-sensitive cardiac syncytia in silico.(a–f) Computational models of light-sensitive cell monolayers with light sensitivity inscribed by gene delivery (GD; a–c) or cell delivery (CD; d–f). Heterogeneous spatial distributions of ChR2-expressing cells (transduced myocytes in (a–c), donor cells in (d–f)) were generated using a previously-described stochastic algorithm25; for each distribution type (I, UL, UH) and delivery mode (CD or GD), parameter values for density and clustering (D and C; shown in black boxes) were chosen to ensure a good match between in silico and in vitro monolayers (compare (a–f) to Fig. 2a–f) For GD-I (a), the algorithm was invoked with distinct parameter combinations in 3 distinct regions: the central target (blue shaded; 1.6% of total monolayer area), an outer ring (red shaded; 1.3% of monolayer area), and the surrounding area. For CD-I (d), model generation involved two invocations of the distribution algorithm: first, to produce a large central cluster (blue; 4.5% of monolayer are); second, to deliver light-sensitive cell clusters to the peripheral region. (g) Normalized histogram showing occurrence rates of light-sensitive cell cluster sizes for in vitro monolayers of the UL spatial distribution; histograms for GD (Fig. 2b) and CD (Fig. 2e) modes are compared. (h) Same as (g), but for in silico cell monolayer models. Main panel scale bars in (a–f) are 2 mm; inset panel scale bars are 1 mm.

Mentions: Image-based templating and a stochastic algorithm2328 with additional regularization steps were used to derive parametric computational representations of the experimentally-designed transgene distributions; spatial patterns representing a much wider range of conditions than feasible to characterize experimentally were also generated and analysed. The parametric in silico representations used opsin density (D) and clustering (C) parameters (see Materials and Methods section), tuned to faithfully capture the experimental in vitro transgene distributions, the latter obtained using high-resolution panoramic confocal images (compare Fig. 2a–f and Fig. 3a–f). The density (D) parameter determined the proportion of opsin-expressing units by volume, while clustering (C) quantified the degree of aggregation of light-sensitive regions.


Optogenetics-enabled assessment of viral gene and cell therapy for restoration of cardiac excitability.

Ambrosi CM, Boyle PM, Chen K, Trayanova NA, Entcheva E - Sci Rep (2015)

Patterned light-sensitive cardiac syncytia in silico.(a–f) Computational models of light-sensitive cell monolayers with light sensitivity inscribed by gene delivery (GD; a–c) or cell delivery (CD; d–f). Heterogeneous spatial distributions of ChR2-expressing cells (transduced myocytes in (a–c), donor cells in (d–f)) were generated using a previously-described stochastic algorithm25; for each distribution type (I, UL, UH) and delivery mode (CD or GD), parameter values for density and clustering (D and C; shown in black boxes) were chosen to ensure a good match between in silico and in vitro monolayers (compare (a–f) to Fig. 2a–f) For GD-I (a), the algorithm was invoked with distinct parameter combinations in 3 distinct regions: the central target (blue shaded; 1.6% of total monolayer area), an outer ring (red shaded; 1.3% of monolayer area), and the surrounding area. For CD-I (d), model generation involved two invocations of the distribution algorithm: first, to produce a large central cluster (blue; 4.5% of monolayer are); second, to deliver light-sensitive cell clusters to the peripheral region. (g) Normalized histogram showing occurrence rates of light-sensitive cell cluster sizes for in vitro monolayers of the UL spatial distribution; histograms for GD (Fig. 2b) and CD (Fig. 2e) modes are compared. (h) Same as (g), but for in silico cell monolayer models. Main panel scale bars in (a–f) are 2 mm; inset panel scale bars are 1 mm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4664892&req=5

f3: Patterned light-sensitive cardiac syncytia in silico.(a–f) Computational models of light-sensitive cell monolayers with light sensitivity inscribed by gene delivery (GD; a–c) or cell delivery (CD; d–f). Heterogeneous spatial distributions of ChR2-expressing cells (transduced myocytes in (a–c), donor cells in (d–f)) were generated using a previously-described stochastic algorithm25; for each distribution type (I, UL, UH) and delivery mode (CD or GD), parameter values for density and clustering (D and C; shown in black boxes) were chosen to ensure a good match between in silico and in vitro monolayers (compare (a–f) to Fig. 2a–f) For GD-I (a), the algorithm was invoked with distinct parameter combinations in 3 distinct regions: the central target (blue shaded; 1.6% of total monolayer area), an outer ring (red shaded; 1.3% of monolayer area), and the surrounding area. For CD-I (d), model generation involved two invocations of the distribution algorithm: first, to produce a large central cluster (blue; 4.5% of monolayer are); second, to deliver light-sensitive cell clusters to the peripheral region. (g) Normalized histogram showing occurrence rates of light-sensitive cell cluster sizes for in vitro monolayers of the UL spatial distribution; histograms for GD (Fig. 2b) and CD (Fig. 2e) modes are compared. (h) Same as (g), but for in silico cell monolayer models. Main panel scale bars in (a–f) are 2 mm; inset panel scale bars are 1 mm.
Mentions: Image-based templating and a stochastic algorithm2328 with additional regularization steps were used to derive parametric computational representations of the experimentally-designed transgene distributions; spatial patterns representing a much wider range of conditions than feasible to characterize experimentally were also generated and analysed. The parametric in silico representations used opsin density (D) and clustering (C) parameters (see Materials and Methods section), tuned to faithfully capture the experimental in vitro transgene distributions, the latter obtained using high-resolution panoramic confocal images (compare Fig. 2a–f and Fig. 3a–f). The density (D) parameter determined the proportion of opsin-expressing units by volume, while clustering (C) quantified the degree of aggregation of light-sensitive regions.

Bottom Line: Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias).Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation.More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY.

ABSTRACT
Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias). In addition to electronic device therapy (i.e. implantable pacemakers and cardioverter/defibrillators), biological approaches have recently been explored to restore pacemaking ability and to correct conduction slowing in the heart by delivering excitatory ion channels or ion channel agonists. Using optogenetics as a tool to selectively interrogate only cells transduced to produce an exogenous excitatory ion current, we experimentally and computationally quantify the efficiency of such biological approaches in rescuing cardiac excitability as a function of the mode of application (viral gene delivery or cell delivery) and the geometry of the transduced region (focal or spatially-distributed). We demonstrate that for each configuration (delivery mode and spatial pattern), the optical energy needed to excite can be used to predict therapeutic efficiency of excitability restoration. Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation. More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

No MeSH data available.


Related in: MedlinePlus